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Built-in Fault-Tolerant Computing Paradigm for Resilient Large-Scale Chip Design
With the end of Dennard scaling and Moore’s law, IC chips, especially large-scale ones, now face more reliability challenges, and reliability has become one of the mainstay merits of VLSI designs. In this context, this book presents a built-in on-chip fault-tolerant computing paradigm that seeks to combine fault detection, fault diagnosis, and error recovery in large-scale VLSI design in a unified manner so as to minimize resource overhead and performance penalties. Following this computing paradigm, we propose a holistic solution based on three key components: self-test, self-diagnosis and self-repair, or “3S” for short. We then explore the use of 3S for general IC designs, general-pu...
Human Centered Computing
This book constitutes thoroughly reviewed, revised and selected papers from the 6th International Conference on Human Centered Computing, HCC 2020, held in virtually, due to COVID- 19, in December 2020. The 28 full and 20 short papers presented in this volume were carefully reviewed and selected from a total of 133 submissions. The conference focuses on the following three main themes as follows: Data such as Data Visualization, Big Data, Data Security, Hyper connectivity such as Internet of Things, Cloud Computing, Mobile Network and Collaboration such as Collective Intelligence, Peer Production, Context Awareness and much more.
New evidence on the Psychological Impacts and Consequences of Covid-19 on Mental Workload Healthcare Workers in Diverse Regions in the World
The Covid-19 pandemic has had a significant global impact on our daily lives. At the center of the pandemic are healthcare workers who have faced a great psychological burden in attempting to counter the virus in both short and long terms contexts. The goal of this Research Topic is to offer new evidence on the mental health experiences of healthcare workers under the Covid 19 pandemic by taking on a broad global perspective. We are particularly interested in new evidence that extends the existing meta-analyses on the topic to build further knowledge.
Low-Power Computer Vision
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
Data Science
This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mi...
Technologies for E-Learning and Digital Entertainment
This book constitutes the refereed proceedings of the First International Conference on E-learning and Games, Edutainment 2006, held in Hangzhou, China in April 2006. The 121 revised full papers and 52 short papers presented together with the abstracts of 3 invited papers and those of the keynote speeches cover a wide range of topics, including e-learning platforms and tools, learning resource management, practice and experience sharing, e-learning standards, and more.
Evolution, Molecular Mechanisms and the Strategies to Combat Antimicrobial Resistance (AMR): A One Health Approach
Humanity, animals, and the ecosystem are seriously threatened by antimicrobial resistance. A significant reason is the inappropriate use of antimicrobials in the human, animal, and environmental sectors as well as the global spread of resistant bacteria and resistance genes. Lately, the use of antimicrobials in animals raised for human consumption has become a significant public health issue. The environment also has a big impact in the interface between human and animal health through biodiversity loss, climate change, and pollution. A sustainable balance and optimization of the health of people, animals, and ecosystems are the goals of the comprehensive, unifying approach known as “One Health”. To battle and contain antimicrobial resistance within a One Health strategy, rigorous antimicrobial resistance surveillance along with strict execution of the core plan and action should be carried out at various sectors individually and collectively, at the national and worldwide level.
The 2021 International Conference on Smart Technologies and Systems for Internet of Things
This book contains papers presented at the 2021 International Conference on Smart Technologies and Systems for Internet of Things, held on November 26–27, 2021, in Shanghai, China. It covers topics like distributed processing for sensor data in CPS networks, approximate reasoning and pattern recognition for CPS networks, distributed processing in mobile networking, data analytics for social media sensor data integration, data platforms for efficient integration with CPS networks, virtualized and cloud-oriented resources for data processing for CPS networks, machine learning algorithms for CPS networks, data security and privacy in CPS networks, sensor fusion algorithms, sensor signal processing, data acquisition and preprocessing technology, intelligent computing, data mining methods and algorithms, big data system solutions and tools platform, intelligent control and intelligent management, and operational situation awareness utilizing big data-driven intelligence. It caters to postgraduate students, researchers, and practitioners specializing and working in related areas.
Distributional Semantics
Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to construct semantic representations, a computational framework for acquiring meaning from language data, and a cognitive hypothesis about the role of language usage in shaping meaning. This book aims to build a common understanding of the theoretical and methodological foundations of distributional semantics. Beginning with its historical origins, the text exemplifies how the distributional approach is implemented in distributional semantic models. The main types of computational models, including modern deep learning ones, are described and evaluated, demonstrating how various types of semantic issues are addressed by those models. Open problems and challenges are also analyzed. Students and researchers in natural language processing, artificial intelligence, and cognitive science will appreciate this book.
